A Study on the Detection and Prevention of Cyber Attacks using Machine Learning Algorithms

Authors

  • Muniba Murtaza1 Position Trainer
  • Muhammad Saeed Ahmad2 Assistant professor, Government Sadiq College Women University Bahawalpur
  • Adnan Bukhari Syed3 Department of ICT Directorate, Quaid-i-Azam University Islamabad
  • Arsalan Khan4 Department of Quality enhancement cell (QEC), Quaid-i-Azam University Islamabad

Abstract

This study explores the use of machine learning algorithms to detect and prevent cyber attacks. The research focuses on several widely used models, including Decision Trees, Support Vector Machines (SVM), Random Forests, and Neural Networks, evaluating their performance on datasets related to network traffic, intrusion detection, and malware classification. Preprocessing techniques such as data cleaning, feature selection, and balancing were applied to optimize the datasets for model training. The results show that Neural Networks outperformed the other algorithms in terms of accuracy, precision, recall, and F1-score, followed by Random Forests. This study highlights the importance of machine learning in cyber security, demonstrating its potential to detect complex attack patterns and improve real-time threat detection systems.

Keywords: Machine, learning, algorithms, cyber, attacks, Decision Trees, Support Vector Machines (SVM), Random Forests, Neural Networks.

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Published

2024-12-22

How to Cite

Muniba Murtaza1, Muhammad Saeed Ahmad2, Adnan Bukhari Syed3, & Arsalan Khan4. (2024). A Study on the Detection and Prevention of Cyber Attacks using Machine Learning Algorithms. Spectrum of Engineering Sciences, 2(4), 433–452. Retrieved from https://sesjournal.com/index.php/1/article/view/85

Issue

Vol. 2 No. 4 (2024): Spectrum of engineering sciences

Section

Articles